Method and system for detecting the occurrence of an interaction event via trajectory-based analysis

ABSTRACT

Disclosed is a method and system for detecting an interaction event between two or more objects in a surveillance area, via the application of heuristics to trajectory representations of the static or dynamic movements associated with the objects. According to an exemplary embodiment, trajectory interaction features (TIFs) are extracted from the trajectory representations and heuristics are applied to the TIFs to determine if an interaction event has occurred, such as a potential illegal drug deal involving two or more pedestrians.

BACKGROUND

This disclosure relates to image processing methods and systems for thedetection of events including an interaction of two or more objects,such as, but not limited to, two or more pedestrians. More specifically,this disclosure, and the exemplary embodiments described herein, relatesto the detection of potentially illegal activity involving two or morepeople, such as, but not limited to, a potential drug deal between twopeople and the notification of a central processing system or otherparty regarding the detected event.

Police and Public Safety data is growing at an astounding rate, and isexpected to double every two years. Some data currently collectedoriginates from a wide variety of sources, including the emergencytelephone response system 911, CAD (Computer-Aided Dispatch), mobile,FBR (Field-Based Reporting), RMS (Record Management Sources), Jail,Radio, GPS (Global Positioning Systems) and other police and publicsafety systems. In addition, many cities have video camera surveillancesystems which are manually monitored and/or accessed by personal after acrime has been committed or incident has occurred where further reviewof the crime or incident is necessary and/or warranted.

One currently available system offered by Xerox® and referred to as aPolice Business Intelligence (PBI) system, is an information discoverytool for use with public safety agencies. PBI provides enhancedcapabilities for data integration, analysis, visualization anddistribution of information within and across agencies. PBI canassimilate data from all interconnected departments' databases as wellas external sources to provide actionable insight for public safetycommanders, allowing for rapid, fact-based decision making.

Provided herein are automated methods and systems for detecting anoccurrence of an interaction event of two or more pedestrians using avideo camera towards a surveilled area.

INCORPORATION BY REFERENCE

-   FELZENSZWALB et al., “Object Detection with Discriminatively Trained    Part Based Models,” IEEE Transactions on Pattern Analysis and    Machine Intelligence, Vol. 32, No. 9, September 2010;-   LUVISON et al., “Automatic Detection of Unexpected Events in Dense    Areas for Video surveillance Applications, Video Surveillance”,    Prof. Weiyao Lin (Ed.), ISBN: 978-953-307-436-8, InTech, DOI:    10.5772/15528 (2011). Available from:    http://www.intechopen.com/books/video-surveillance/automatic-detection-of-unexpected-events-in-dense-areas-for-videosurveillance-applications;    and-   STAUFFER et al., “Adaptive Background Mixture Models for Real-Time    Tracking,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recog.,    vol. 2, pp. 246-252, 1999, are incorporated herein by reference in    their entirety.

BRIEF DESCRIPTION

In one embodiment of this disclosure, described is acomputer-implemented method for automatically detecting an occurrence ofan interaction event of two or more people concurrently present in asurveilled area using a video camera directed towards the surveilledarea, the method comprising: a) acquiring a video stream from the videocamera, the video stream including a temporal sequence of video framesincluding the surveilled area within a field of view (FOV) associatedwith the video camera; b) detecting and tracking two or more peoplewithin a common temporal sequence of video frames included in the videostream, and generating a trajectory of each person tracked within thefirst common temporal sequence of video frames; c) processing thetrajectories of the tracked people to extract one or more trajectoryinteraction features (TIFs) associated with the trajectories of the twoor more people tracked within the first common temporal sequence ofvideo frames; and d) applying predefined heuristics to the extractedTIFs to detect an interaction event associated with the predefinedheuristics has occurred between at least two people of the two or morepeople tracked within the first common temporal sequence of videoframes.

In another embodiment of this disclosure, described is a video systemfor automatically detecting an occurrence of an interaction event of twoor more people concurrently present in a surveilled area comprising: avideo camera with an associated FOV (field-of-view) directed towards thesurveilled area; and a video processing system operatively connected tothe video camera, the video processing system configured to: a) acquirea video stream from the video camera, the video stream including atemporal sequence of video frames including the surveilled area withinthe FOV associated with the video camera; b) detect and track two ormore people within a first common temporal sequence of video framesincluded in the video stream, and generate a trajectory of each persontracked within the first common temporal sequence of video frames; c)process the trajectories of the tracked people to extract one or moretrajectory interaction features (TIFs) associated with the trajectoriesof the two or more people tracked within the first common temporalsequence of video frames; and d) apply predefined heuristics to theextracted TIFs to detect an interaction event has occurred between atleast two people of the two or more people tracked within the firstcommon temporal sequence of video frames.

In still another embodiment of this disclosure, described is a videosystem for automatically detecting an occurrence of an interaction eventof two or more objects concurrently present in a surveilled area, theinteraction event associated with an illegal drug deal between the twoor more objects, comprising: a video camera with an associated FOV(field-of-view) directed towards the surveilled area; and a videoprocessing system operatively connected to the video camera, the videoprocessing system configured to: a) acquire a video stream from thevideo camera, the video stream including a temporal sequence of videoframes including all or part of the surveilled area within all or partof the FOV associated with the video camera; b) detect and track two ormore objects within a first common temporal sequence of video framesincluded in the video stream, and generate a trajectory of each objecttracked within the first common temporal sequence of video frames; c)process the trajectories of the tracked objects to extract one or moretrajectory interaction features (TIFs) associated with the trajectoriesof the two or more objects tracked within the first common temporalsequence of video frames, the TIFs including one or more of a position,a velocity and a relative distance associated with the two or moreobjects within the first common temporal sequence of video frames; andd) apply predefined heuristics to the extracted TIFs to detect aninteraction event has occurred between at least two objects of the twoor more objects tracked within the first common temporal sequence ofvideo frames, the predefined heuristics including a velocity thresholdand a proximity threshold associated with the two or more objectstracked within the first common temporal sequence of video frames,wherein steps b)-d) are repeated for a second common temporal sequenceof video frames, distinct from the first common temporal sequence ofvideo frames, to determine if the interaction event has occurred betweenat least two objects of the two or more objects tracked within thesecond common temporal sequence of video frames.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system for automatically detecting anoccurrence of an interaction event of two or more people concurrentlypresent in a surveilled area according to an exemplary embodiment ofthis disclosure.

FIGS. 2A and 2B are images captured of surveilled areas using a near/midFOV (field-of-view) video camera (FIG. 2A) and a far FOV video camera(FIG. 2B).

FIG. 3 is an illustration of an interaction event, i.e., potential drugdeal event on/near a pedestrian walkway, from which heuristic rules arederived for operation on video-based trajectories associated with thepedestrians to detect the interaction event, according to an exemplaryembodiment of this disclosure.

FIG. 4 is a plot of trajectories generated from mid-field video, theplot graphs a j-axis representative of the pixel location of severalpedestrians vs. a corresponding frame number of the processed video,according to an exemplary embodiment of this disclosure.

FIGS. 5A, 5B and 5C are illustrations of trajectory interaction features(TIFs) generated for pedestrian P1 and P6 trajectories, shown in FIG. 4,indicating a probable drug deal, where FIG. 5A illustrates pedestrianlocation TIFs for pedestrians P1 and P6, FIG. 5B illustrates a relativedistance TIF, indicating the relative distance between pedestrians P1and P6, and FIG. 5C illustrates velocity TIFs for pedestrians P1 and P6,according to an exemplary embodiment of this disclosure.

FIGS. 6A, 6B and 6C are illustrations of TIFs generated for pedestriansP2 and P3 trajectories shown in FIG. 4, indicating a probablewalk-together-pair of pedestrians, where FIG. 6A illustrates pedestrianlocation TIFs for pedestrians P2 and P3, FIG. 6B illustrates a relativedistance TIF, indicating the relative distance between pedestrians P2and P3, and FIG. 6C illustrates velocity TIFs for pedestrians P2 and P3,according to an exemplary embodiment of this disclosure.

FIGS. 7A, 7B and 7C are illustrations of TIFs generated for pedestriansP1 and P5 trajectories shown in FIG. 4, indicating a probablypass-by-pair of pedestrians, where FIG. 7A illustrates pedestrianlocation TIFs for pedestrians P1 and P5, FIG. 7B illustrates a relativedistance TIF, indicating the relative distance between pedestrians P1and P5, and FIG. 7C illustrates velocity TIFs for pedestrians P1 and P5,according to an exemplary embodiment of this disclosure.

FIGS. 8A, 8B and 8C are examples of processed mid-field video, accordingto an exemplary embodiment of this disclosure, detecting a potentialdrug deal event and labelling the relevant video frame(s) where theevent occurred.

FIGS. 9A, 9B and 9C are examples of processed far-field video, accordingto an exemplary embodiment of this disclosure, detecting a potentialdrug deal event and labeling the relevant video frame(s) where the eventoccurred.

FIG. 10 is a plot of trajectories generated from far-field video, theplot graphs a j-axis representative of the pixel location of severalpedestrians vs. a corresponding frame number of the processed video,according to an exemplary embodiment of this disclosure.

FIGS. 11A, 11B and 11C are illustrations of TIFs generated forpedestrian P1 and P6 trajectories, shown in FIG. 10, indicating aprobable drug deal where FIG. 11A illustrates pedestrian location TIFsfor pedestrians P1 and P6, FIG. 11B illustrates relative distance TIFs,indicating the relative distance between pedestrians P1 and P6, and FIG.11C illustrates velocity TIFs for pedestrians P1 and P6, according to anexemplary embodiment of this disclosure.

FIGS. 12A, 12B and 12C are illustrations of TIFs generated forpedestrian P12 and P13 trajectories, shown in FIG. 10, indicating aprobable walk-together-pair where FIG. 12A illustrates pedestrianlocation TIFs for pedestrians P12 and P13, FIG. 12B illustrates relativedistance TIFs, indicating the relative distance between pedestrians P12and P13, and FIG. 12C illustrates velocity TIFs for pedestrians P12 andP13, according to an exemplary embodiment of this disclosure.

FIGS. 13A, 13B and 13C are illustrations of TIFs generated forpedestrian P11 and P12 trajectories, shown in FIG. 10, indicating aprobable walk-follow-pair where FIG. 13A illustrates pedestrian locationTIFs for pedestrians P11 and P12, FIG. 13B illustrates relative distanceTIFs, indicating the relative distance between pedestrians P11 and P12,and FIG. 13C illustrates velocity TIFs for pedestrians P11 and P12,according to an exemplary embodiment of this disclosure.

FIGS. 14A and 14B are a system diagram of a Police Business Intelligence(PBI) System including an Event Detection Module incorporatinginteraction event detection according to an exemplary embodiment of thisdisclosure.

DETAILED DESCRIPTION

The present disclosure provides a method and system for detectingevent(s) including an interaction of two or more objects, such as, butnot limited to, potential drug deal activity involving two or morepedestrians via trajectory-based analysis. When supplied with propercamera calibration information or direct estimation of humanheights/widths in pixels, this method can be applied effectively tosurveillance videos ranging from near-field/mid-field view to far-fieldview. An exemplary embodiment of the disclosed system is shown in FIG. 1and includes:

A Video Acquisition Module 105, which acquires video of a scene beingsurveilled;

A Person Detection and Tracking Module 110, which detects the presenceof person(s), tracks him/her/them in the entire field of view or inpre-determined regions in the scene, and reports the (on-going)trajectory of tracked person(s);

A Trajectory Interaction Feature Extraction Module 115, which analyzesthe trajectories of tracked persons and extracts trajectory interactionfeatures (TIFs) from multiple trajectories that co-occur in the scene;

A Potential Drug Deal Activity Detection Module 120, which determineswhether a potential drug deal activity has occurred through rule-basedanalysis on the extracted TIFs;

An Evidence Collection Module 125, which collects the temporal evidenceof detected potential drug deal events to determine the probability orlevel of confidence that a drug deal activity has occurred; and

An Alarm and Notification Module 130, which alerts and notifies acentral system or party of interest of the detected event.

More detailed description about each module and how they work togetheras a system to accomplish the detection of potential drug deal eventsare presented below.

Video Acquisition Module (105)

The Video Acquisition Module 105 includes a camera that acquires videoof a scene being surveilled. It is to be understood that it is withinthe scope of this disclosure and the exemplary embodiments describedherein that video and FOVs associated with the acquired video caninclude near-field/mid-field/overhead views, as well as other video andFOVs from various distances and perspectives. Example video frames ofthe field of views of a near-field/mid-field security camera and afar-field security camera are shown in FIGS. 2A and 2B. The acquiredvideos are streamed or archived and analyzed by the disclosed system todetect occurrences of potential drug deal activity. The camera does notneed to be specialized and can be a typical visible or NIR (NearInfrared) video surveillance camera operating at conventional framerates, for example as 15, 30 or 60 frames/sec and resolutions such as1920×1080, 1280×1024, 1280×720, 1280×960, 1280×1024, 1108×832, 2048×1536pixels. The acquired videos can be streamed/streaming to the analysismodules discussed later to perform real-time or near real-time detectionof potential drug deal events (referred as on-line processing later).They can also be archived and transmitted to the analysis modules toperform detection of potential drug deal events (referred as off-lineprocessing).

A Person Detection and Tracking Module (110)

The Person Detection and Tracking Module 110 analyzes the acquiredvideos (on-line or off-line) and detects the presence of person(s),tracks him/her/them in the entire field of view or in pre-determinedregions in the scene, and reports the (on-going) trajectory of trackedperson(s). Generally, two different types of tracking can beimplemented, either (a) multiple Single Object Tracking (SOT)algorithms, or (b) a single Multiple Object Tracker (MOT) algorithm canbe used. See Shuangyan Yi, Zhenyu He, Xinge You, and Yiu-Ming Cheung,“Single object tracking via robust combination of particle filter andsparse representation,” Signal Processing, Vol. 110, pp. 178-187,(2014); and Shunli Zhang, Sicong Zhao, Yao Sui, Li Zhang, “Single ObjectTracking With Fuzzy Least Squares Support Vector Machine,” IEEETransactions on Image Processing, 2015, Volume: 24 Issue: 12, Pages:5723-5738, herein fully incorporated, for additional information on SOTalgorithms. See Chen-Chien Hsu, Yung-Ching Chu, Ming-Chih Lu, “Hybridmultiple-object tracker incorporating Particle Swarm Optimization andParticle Filter,” 2013 International Conference on System Science andEngineering (ICSSE), Pages: 189-193 and Hamed Moradi Pour and SaeidFazli, “An Advanced Real-Time Multiple Object Tracker in Variant OutdoorEnvironments,” J. Appl Computat Math 2012, vol 1, issue 5, herein fullyincorporated, for additional information on MOT algorithms.

In the first case (multiple SOT), detection is performed once for eachperson, at which point a single object tracker is initialized. Accordingto this approach, detection can be limited to specific regions ofinterest, which may include for example expected points of entrance intothe field of view of the camera. Detection may be performed in differentways. Temporal differencing algorithms can detect objects in motion inthe scene; alternatively, background subtraction, which requires theestimation of the stationary scene background, followed by subtractionof the estimated background from the current frame can detect foregroundobjects (which include objects in motion). The output of either approachis a binary mask with the same pixel dimensions as the input video, andhaving values equal to 0 where no motion/foreground objects are detectedand values equal to 1 at pixel locations where motion/foreground objectsare detected. This detection mask is usually post-processed viamorphological operations which discard detected objects with size andorientation outside pre-determined ranges determined by the geometry ofthe capture. Alternatively, computer vision techniques for objectrecognition and localization can be used on still images (e.g., singlevideo frames). These techniques typically entail a training stage wherethe appearance of multiple sample objects in a given feature space(e.g., Deep features, Harris Corners, SIFT, SURF, HOG, LBP, deep orlearned features, etc.) is fed to a classifier (e.g., Neural Net,decision tree, SVM, EM, k-NN, clustering algorithms, etc.) that istrained on the available sample feature representations. The trainedclassifier is then applied to features extracted from frames of interestand outputs the parameters of bounding boxes (e.g., location, width andheight) surrounding the matching candidates. Once a person has beendetected, a SOT is assigned to the person. SOTs determine the locationof the object being tracked by building appearance models of the imageregion where the detection was triggered, and finding candidate regionsin subsequent frames with visual characteristics that best match theappearance of the initial detection. Examples of such trackers includetemplate-based, mean-shift, particle filter, the circulant shift kernel(CSK), and the adaptive color attributes tracker. According to anexemplary embodiment of the algorithm, an adaptive color attributestracker is used. However, the provided method and system istracker-agnostic.

In the second scenario (single MOT), detection is performed on aframe-by-frame basis across the full field of view. Although the samedetection techniques as described above can be used, and since an MOTalgorithm continuously relies on monitoring the detections, motion-baseddetectors are not as well-suited to operate in this scenario. An MOTtakes the full set of detections at each frame, and models detectionerrors and target motions to link detections with the most likelytrajectories. In essence, an MOT solves a correspondence problem of themultiple detections across time.

The output of this module is a set of spatiotemporal sequences, one foreach detected object, each describing the location, and possibly thesize (in the form of a bounding box or a blob of connected pixels) ofeach of the people being tracked.

Trajectory Interaction Feature Extraction Module (115)

The Trajectory Interaction Feature Extraction Module 115 analyzes thetrajectories of tracked persons (outputs from the Person Detection andTracking Module 110) and extracts trajectory interaction features (TIFs)from multiple trajectories that co-occur in the scene. This module canbe implemented in various forms (time-scales) depending on theapplications and offerings. Described below are several options for thisimplementation.

First, smoothing techniques are applied such as convolution, curvefitting, AR (Autoregressive), MA (Moving Average) or ARMA(Autoregressive-Moving-Average), etc., to smooth the trackedtrajectories. The levels of smoothing depend on theperformance/characteristics of the person tracker, and areapplication/module dependent. For the tracker used in ourimplementation, temporal smoothing over ˜4 sec periods was sufficient.Many smoothing methods can work for this task. However, some may be moresuited than others depending on the time-scale used in the module, whichwill be further discussed below. Note that smoothing significantlybenefits the disclosed method and system because important featuresneeded for this application are the velocities and the level of“stationarity” of the persons involved as shown in FIG. 3 which depictsa potential drug deal interaction event where pedestrians P325, P330 andP335 are “hanging out” and pedestrian P320 travels along path 305 topath 310, interacts with one or more of pedestrians P325, P330 and P335,then pedestrian P320 moves on, traveling along path 315 to path 305, ormakes a U-turn and travels back towards the initial location ofpedestrian P320 depicted in FIG. 3. Small levels of noise in atrajectory can get amplified when using it to compute velocities. Oncethe trajectories are smoothed, relevant features are extracted from thesmoothed trajectories for later use. The activity of our interest,illustrated in FIG. 3, involves at least two persons interacting witheach other. Hence relevant features must be extracted from single andmultiple trajectories. In particular, temporal features extractedinclude individual position, individual velocity, and relative distancesbetween persons of interest. These features can be extracted in anoffline or online manner, as described below, depending on theapplication, and these options affect several choices for implementingthis module's algorithm.

Off-line operation: this assumes that the full trajectory has beenextracted using the preceding module, i.e., the Person Detection andTracking Module 110. In this scenario, simpler methods can be used forsmoothing and feature extraction since all data is available at the timeof processing. This, however, limits the usage of the disclosed methodand system to after-the-fact alerts, e.g., for providing evidence incourt or to notify authorities of a location or person(s) of interest.For smoothing, all methods mentioned above (e.g., curve fitting,convolution or AR) can be applied here. For feature extraction, usingtwo trajectories as an example, let

smoothed trajectory, (i_(t) ^(A),j_(t) ^(A)), t=t_(S) ^(A), . . . ,t_(E) ^(A) correspond to person A; and

-   -   smoothed trajectory, (i_(t) ^(B),j_(t) ^(B)), t=t_(S) ^(B), . .        . , t_(E) ^(B) correspond to person B,        where (i,j) are the row and column pixel coordinates,        respectively, and t is time (or frame number), with S and E        denoting start and end times, respectively, for a given person.        In one embodiment, the Trajectory Interaction Features (TIFs)        between A and B may be five temporal profiles of a length equal        to the overlap time duration of their trajectories. In short, in        this embodiment, the TIFs are the positions and velocities of        both persons and the distance between them during the time        periods that both are being tracked. For the case where two        persons have never co-appeared in the videos, no further        analysis is performed because the overlap time duration is zero.        The overlap time duration and five temporal profiles are        expressed mathematically below.

Overlap time duration, min(t _(E) ^(A) ,t _(E) ^(B))−max(t _(S) ^(A) ,t_(S) ^(B)),

(TIF) position of person A at time t, p _(t) ^(A)=(i _(t) ^(A) ,j _(t)^(A)),

(TIF) position of person B at time, p _(t) ^(B)=(i _(t) ^(B) ,j _(t)^(B)),

(TIF) velocity of person A at time, v _(t) ^(A)=√{square root over ((i_(t) ^(A) −i _(t-1) ^(A))²+(j _(t) ^(A) −j _(t-1) ^(A))²)},

(TIF) velocity of person B at time, v _(t) ^(B)=√{square root over ((i_(t) ^(B) −i _(t-1) ^(B))²+(j _(t) ^(B) −j _(t-1) ^(B))²)},

(TIF) relative distance between the persons at time t d _(t)^(B)=√{square root over ((i _(t) ^(A) −i _(t) ^(B))²+(j _(t) ^(A) −j_(t) ^(B))²)}.

Note that, in some embodiments, the outputs of the person detection andtracking module 110 may include the size (e.g., bounding box) of thedetected person in addition to his/her position. In alternativeembodiments, TIFs can be computed via more elaborate formulae. In oneembodiment, instead of computing the TIF d_(t) ^(AB) using Euclideandistances between two points (the positions of person A and person B),TIF d_(t) ^(AB) can represent the “distance” between two regions (e.g.,bounding boxes of A & B or blobs of A & B). According to thisembodiment, TIF d_(t) ^(AB) can be computed as the minimal distancebetween two regions or minimal distance between two regions along apreselected direction (e.g., road plane). The benefit of this extendeddefinition of distance is that it can take into account the pose (due tocameras or persons) of the objects. This is especially useful fornear-field/mid-field views. In the case of far-field view, the boundingboxes of persons are typically small and either embodiment would workwell.

Similarly, in some embodiments, the TIFs describing the velocity ofmotion of a person may be extended from point calculation to regioncalculation. As an example, instead of calculating velocity as theframe-to-frame positional change of centroids of the detected humanblob, it can be estimated as a function (e.g., the average, weightedaverage, etc.) of the velocities of various part of the human blob(e.g., using optical flow calculation of the human blob frame-to-frame).This embodiment would be particularly efficient when the camera view isnear-field/mid-field where we would prefer to use the true velocity ofperson rather than the false velocity of the person coming from upperbody movement. In such scenario, the average speed as estimated by theframe-to-frame displacement of the centroid of the detected human blob(centroids may move due to upper body movement) would not be as accurateas averaging velocities of various parts of the human.

FIG. 4 graphically illustrates an analysis of the mid-field video of thescene in FIG. 2(a). In FIG. 2(a) the pedestrian walkway is roughlyaligned with the j axis shown in FIG. 4, so for simplicity plotted isthe j coordinate of the trajectory of each person tracked as a functionof time in FIG. 4, i.e., frame number. 7 pedestrians were tracked inthis example where the pedestrians entered the scene from the right(high j). If they continued along the walk way they exit the scene witha low j value. Denoting the persons as P1, P2, P3, P4, P5, P6 and P7 inthe graph, individually, the traces can be understood as follows:

Persons P2, P3 and P4 walked continuously along the walkway withoutpause.

Person P1 (which is person A for an analysis provided below) enters thescene and remains at a spot for a relatively long time. Hence, person P1is a potential dealer.

Persons P6 and P7 enter from the right, have a dwell time, and exit tothe right.

FIGS. 5A-5C show the five TIFs between person P1 (A) and person P6,which is referred to as B for understanding the TIFs and how they relateto recognizing a drug deal. FIG. 5A shows trajectories p_(t) ^(A) &p_(t) ^(B) plotted for their overlap time duration, roughly from frame500˜700. FIG. 5B shows the distance d_(t) ^(AB) between the pair as afunction of time over the overlap duration time. It can be seen that fora period of time, roughly frames 525 to 650, they are quite close (<150pixels away). FIG. 5C shows their velocities v_(t) ^(A) and v_(t) ^(B)where it is indicated that they are nearly stationary (velocity <5pixels/frame) for that period of time. By comparing these metrics to themetrics of other pedestrians, it can be understood how they can be usedto identify a drug deal.

For comparison, consider FIGS. 6A-6C which show the TIFs for person P2and person P3. Their trajectories are shown in FIG. 6A. FIG. 6B showsthat they stay in the range of 20 to 60 pixels from each otherthroughout their complete overlap time duration, and FIG. 6C shows theirvelocities are almost identical (≈15 pixels/frame) during that timeperiod. These metric can be used to detect that this pair is walkingtogether throughout the scene.

FIGS. 7A-7C provides another example which shows the TIFs for person P1and person P5 in a “pass-by pair” scenario. The plots of FIGS. 7A-7Cshow that the people involved tend to be at a significant distance fromeach other (FIG. 7B), and they don't have a common period of near zerovelocity. From these trajectory features, it can be concluded thatperson P5 passed by person P1 without any interaction taking place.These are some example pair-wise TIFs from various two-person trajectoryanalyses. By performing exhaustive pair-wise trajectory analyses andperforming the algorithms discussed below, drug deal actions aredetectable and were detected in test videos.

On-line operation: this assumes that videos are streaming into thedisclosed system for continuous processing. In one embodiment wherereal-time person tracking can be achieved, the trajectories are receivedup to the current time. In this scenario, smoothing techniques such asAR or MA may be more suitable than curve fitting since these smoothingmethods only require a small amount of buffering to store a small amountof recent data. For feature extraction, the process is the same as forthe off-line operation with the exception that the length of thetrajectory interaction features may change over time, growing in lengthas persons start to co-appear and eventually stop at the same length asthe off-line operation once the interaction stops. Running the moduleon-line provides better offerings including both real-time alerts andafter-the-fact alerts. However, the processing requirements are muchhigher and the algorithm has to be more conservative in collectingevidence before raising the alert.

A Potential Drug Deal Activity Detection Module (120)

The Potential Drug Deal Activity Detection Module 120 determines whethera potential drug deal activity has occurred through heuristic rule-basedanalysis on the extracted trajectory interaction features (outputs fromModule 115). The illustration on FIG. 3 provides some intuition for thesemantic rules needed to detect potential drug deal activity:approaching, stopping at proximity for a while, and leaving. All threesub-actions can be detected from analyzing the five temporal “trajectoryinteraction features” (TIFs) extracted from the Trajectory InteractionFeature Extraction Module 115. According to an exemplary implementation,the following rules are applied for detecting potential drug dealactivity.

Let e_(t) ^(AB), t=max(t_(S) ^(A)+1,t_(S) ^(B)+1), . . . , min(t_(E)^(A),t_(E) ^(B)) be an evidence vector indicating that A and B areparticipating in a drug deal action. Its state in time is determined by:

$e_{t}^{AB} = \left\{ {\begin{matrix}1 & {{{if}\mspace{14mu} {\max \left( {v_{t}^{A},v_{t}^{B}} \right)}} < {{n_{v}({FOV})}\bigcap d_{t}^{AB}} < {\eta_{d}({FOV})}} \\0 & {otherwise}\end{matrix},} \right.$

where e_(t) ^(AB)=1 is interpreted in the Evidence Collection Module 125described below as evidence supporting that a drug deal action hasoccurred. The vector is post processed with additional temporalfiltering such as median filtering to remove detection of low confidenceevents. Note the evidence vector dependency on the velocity threshold n,and the proximity threshold η_(d) on the Field Of View (FOV). As shownin FIGS. 2A and 2B, the two views have a scale 2-3× difference near thesidewalk areas. The algorithm needs to comprehend that in order to berobust across various fields of view in practice. One solution for thisis to perform camera calibration in the field for all the cameras andoperate the disclosed trajectory analysis in physical units.Alternatively, simple approximation can be done without cameracalibration due to information acquired as the system detects and trackspersons. The collected sizes of tracked humans (e.g., heights or widths)can be used as a simple surrogate for adjusting thresholds from onecamera view to another.

Although a simple heuristic rule of setting lower bounds to velocity anddistance among persons of interest works well, other rules can beapplied or adapted over time. For example, the threshold can also besize-dependent. This can be useful to rule out the persons that are notlikely to be involved in the drug deal (e.g., kids). For anotherexample, group size may also be used as a filtering rule given that itis less likely to have a “crowd” interaction with a drug dealer.

An Evidence Collection Module (125)

The Evidence Collection Module 125 collects the temporal evidences ofdetected potential drug deal events (outputs from Module 120) todetermine the probability or level of confidence that drug deal activityhas occurred. In one exemplary embodiment, evidence is collected asfollows. From the previous Event Detection Module 120, every time asingle event of potential drug deal activity (if e_(t) ^(AB) has anynon-zeros) is detected, a count is added to person A and person B abouttheir involvement in the event, for example, the event duration andstart/end time. A record of the counts is maintained indicating thenumber of detected events for those persons. When/if there are commonpersons that are involved with high counts of a detected event, it islikely that drug deal activity has occurred in the scene beingsurveilled. In another embodiment, evidence is accumulated in thefollowing manner. For two individuals, if a sufficient overall count orrun-lengths of 1's occur in e, then there is an indication that thosepersons may be involved in drug dealing activity. If similar evidence isacquired over time for other trajectories, then it is an indication ofregular drug activity. In that case, the stationary person(s) may be thesame over multiple e while the approaching and exiting persons can bethe same or different. For example, in particular embodiments,trajectories of tracked people are processed by the TrajectoryInteraction Feature Extraction Module 115 and analyzed by the PotentialDrug Deal Activity Module 120 over more than one temporal sequence ofvideo frames to determine if a potential drug deal event has occurred.

Alarm and Notification Module (130)

The Alarm and Notification Module 130 alerts and notifies a centralsystem or party of interest of the detected event when the evidencecollection (outputs from Module 125) issues a high probabilityobservation that a drug deal activity has occurred. Appropriate actionsare taken based on the application.

Experimental Results

The disclosed method and system was implemented and tested on videosacquired from a surveillance system. A mix of simulated drug deal eventsand other irrelevant persons passing by were contained in two sets ofvideos acquired with a mid-field view FOV and a far-field view FOV. Thevelocity threshold η_(v) and the proximity threshold η_(d) for themid-field view was 5 pixels/frame and 150 pixels, respectively; whilethe velocity threshold η_(n) and the proximity threshold η_(d) for thefar-field view was 2.5 pixels/frame and 75 pixels, respectively. A scaleof 2 was estimated from the difference of average human sizes betweenthe two views.

FIGS. 8A-8C and FIGS. 9A-9C show examples of how videos analyzed weremarked automatically by the disclosed method. It shows three samplesframes: approaching (FIGS. 8A and 9A), drug deal in progress (FIGS. 8Band 9B), and leaving (FIGS. 8C and 9C), in the process of drug dealactivities for mid-field and far-field videos. Note that boxesidentified with a HT indicate bounding boxes provided by the humantracker. If the trajectory analysis module 120 detects a drug dealevent, i.e., the time when e_(t) ^(AB)≠0, a text label text “Deal EventDetected” is labeled on the upper left corner of the image frame.Additionally, the bounding boxes including those persons involved in thedetected event are highlighted, for example, in the color Red (notshown) or identified with IP indicating they were an involved person.After the action is no longer detected, i.e., the time when e_(t)^(AB)=0, the bounding boxes of tracked person are again identified byHT.

FIGS. 10-13 show results similar to those in FIGS. 4-7 but for far-fieldvideos, which illustrates why the method and system works well.

Note that in the exemplary embodiment described, a drug deal action isdetected through pair-wise TIFs. This approach can be easily extended toanalysis on trajectories involving three or more persons. Furthermore,the exemplary embodiment described does not classify or categorizevarious actions that are not drug deal activity (e.g., walk together,pass-by, walk follow, . . . ;). However, further classification orcategorization of interactions of people can be performed.

With reference to FIGS. 14A and 14B, provided is a system diagram of aPolice Business Intelligence (PBI) system including an Event DetectionModule incorporating event detection according to an exemplaryembodiment of this disclosure. This system is provided to illustrate amanner of incorporating a method for automatically detecting anoccurrence of an interaction event, such as a potential drug deal,including two or more people as described herein, into a centralprocessing system for use with a central public safely and/or lawenforcement system.

The PBI system includes a Centralized Processing System 1404 which isoperatively connected to Law Enforcement Agencies 1402, one or moreVideo Cameras 1408, SNMP Compliant Devices 1410, Vehicle GPSs 1412,Mobile Communication Devices 1414 and a Printer 1416.

The Central Processing System includes a Central Repository Module 1426,a NLS (National Library Service) Module 1428, KPI (Key PerformanceIndicator) Calculation Module 1432, A Database Access Module 1434,Alerting Service Module 1440, a Status Monitoring Module 1430, a VideoGateway Proxy Module 1436 and a Management/Status Web Portal Module1438. The Status Monitoring Module 1430 includes the processing of videoacquired from one or more Video Cameras 1408 to detect an occurrence ofan interaction event of two or more people, such as a potential drugdeal, as previously described.

The Law Enforcement Agency Module 1402 includes a User Source Database1420, Images/Video/Audio Database 1422 and Data Transformation LayerService Module 1424.

Some portions of the detailed description herein are presented in termsof algorithms and symbolic representations of operations on data bitsperformed by conventional computer components, including a centralprocessing unit (CPU), memory storage devices for the CPU, and connecteddisplay devices. These algorithmic descriptions and representations arethe means used by those skilled in the data processing arts to mosteffectively convey the substance of their work to others skilled in theart. An algorithm is generally perceived as a self-consistent sequenceof steps leading to a desired result. The steps are those requiringphysical manipulations of physical quantities. Usually, though notnecessarily, these quantities take the form of electrical or magneticsignals capable of being stored, transferred, combined, compared, andotherwise manipulated. It has proven convenient at times, principallyfor reasons of common usage, to refer to these signals as bits, values,elements, symbols, characters, terms, numbers, or the like.

It should be understood, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the discussion herein,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The exemplary embodiment also relates to an apparatus for performing theoperations discussed herein. This apparatus may be specially constructedfor the required purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods described herein. The structure for avariety of these systems is apparent from the description above. Inaddition, the exemplary embodiment is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the exemplary embodiment as described herein.

A machine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). For instance, a machine-readable medium includes read onlymemory (“ROM”); random access memory (“RAM”); magnetic disk storagemedia; optical storage media; flash memory devices; and electrical,optical, acoustical or other form of propagated signals (e.g., carrierwaves, infrared signals, digital signals, etc.), just to mention a fewexamples.

The methods illustrated throughout the specification, may be implementedin a computer program product that may be executed on a computer. Thecomputer program product may comprise a non-transitory computer-readablerecording medium on which a control program is recorded, such as a disk,hard drive, or the like. Common forms of non-transitorycomputer-readable media include, for example, floppy disks, flexibledisks, hard disks, magnetic tape, or any other magnetic storage medium,CD-ROM, DVD, or any other optical medium, a RAM, a PROM, an EPROM, aFLASH-EPROM, or other memory chip or cartridge, or any other tangiblemedium from which a computer can read and use.

Alternatively, the method may be implemented in transitory media, suchas a transmittable carrier wave in which the control program is embodiedas a data signal using transmission media, such as acoustic or lightwaves, such as those generated during radio wave and infrared datacommunications, and the like.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

3. The computer-implemented method for automatically detecting theinteraction of two or more people according to claim 2, furthercomprising: e) collecting evidence of the detected interaction events instep d), the evidence including one or more of the number of occurrencesof the detected interaction event, the number of occurrences of thedetected interaction event associated with each of the two or morepeople detected within the first and second common temporal sequence ofvideo frames, a time duration and start/end time associated with eachdetected interaction event, a calculated probability of the occurrenceof the interaction event, and an indication of static or dynamicmovement associated with each of the two or more people detected withinthe first and second common temporal sequence of video frames.
 4. Thecomputer-implemented method for automatically detecting the interactionevent of two or more people according to claim 3, further comprising f)communicating an alert to an operatively associated central system, thealert indicating one or more of the number of occurrences of thedetected interaction event, the number of occurrences of the detectedinteraction event associated with each of the two or more peopledetected within the first and second common temporal sequence of videoframes, a time duration and start/end time associated with each detectedinteraction event, a calculated probability of the occurrence of theinteraction event, and an indication of static or dynamic movementassociated with each of the two or more people detected within the firstand second common temporal sequence of video frames.
 5. The computerimplemented method for automatically detecting the interaction event oftwo or more people according to claim 1, wherein the interaction eventis an illegal drug deal between two or more people.
 6. The computerimplemented method for automatically detecting the interaction event oftwo or more people according to claim 5, wherein steps b)-d) arerepeated for a second common temporal sequence of video frames, distinctfrom the first common temporal sequence of video frames, to determine ifthe interaction event has occurred between at least two people of thetwo or more people tracked within the second common temporal sequence ofvideo frames.
 7. The computer-implemented method for automaticallydetecting the interaction event of two or more people according to claim6, further comprising: e) collecting evidence of the detectedinteraction events in step d), the evidence including one or more of thenumber of occurrences of the detected interaction event, the number ofoccurrences of the detected interaction event associated with each ofthe two or more people detected within the first and second commontemporal sequence of video frames, a time duration and start/end timeassociated with each detected interaction event, a calculatedprobability of the occurrence of the interaction event, and anindication of static or dynamic movement associated with each of the twoor more people detected within the first and second common temporalsequence of video frames.
 8. The computer-implemented method forautomatically detecting the interaction event of two or more peopleaccording to claim 7, further comprising: f) communicating an alert toan operatively associated central system, the alert indicating one ormore of the number of occurrences of the detected interaction event, thenumber of occurrences of the detected interaction event associated witheach of the two or more people detected within the first and secondcommon temporal sequence of video frames, a time duration and start/endtime associated with each detected interaction event, a calculatedprobability of the occurrence of the interaction event, and anindication of static or dynamic movement associated with each of the twoor more people detected within the first and second common temporalsequence of video frames.
 9. The computer-implemented method forautomatically detecting the interaction event of two or more peopleaccording to claim 1, wherein the TIFs include one or more of aposition, a velocity and a relative distance associated with the two ormore people within the first common temporal sequence of video frames.10. The computer-implemented method for automatically detecting theinteraction event of two or more people according to claim 9, whereinthe predefined heuristics applied in step d) include the calculation ofan evidence vector state, the evidence vector state calculated as afunction of a velocity threshold and a proximity threshold associatedwith the two or more people tracked within the first common temporalsequence of video frames.
 11. A video system for automatically detectingan occurrence of an interaction event of two or more people concurrentlypresent in a surveilled area comprising: a video camera with anassociated FOV (field-of-view) directed towards the surveilled area; anda video processing system operatively connected to the video camera, thevideo processing system configured to: a) acquire a video stream fromthe video camera, the video stream including a temporal sequence ofvideo frames including the surveilled area within the FOV associatedwith the video camera; b) detect and track two or more people within afirst common temporal sequence of video frames included in the videostream, and generate a trajectory of each person tracked within thefirst common temporal sequence of video frames; c) process thetrajectories of the tracked people to extract one or more trajectoryinteraction features (TIFs) associated with the trajectories of the twoor more people tracked within the first common temporal sequence ofvideo frames; and d) apply predefined heuristics to the extracted TIFsto detect an interaction event has occurred between at least two peopleof the two or more people tracked within the first common temporalsequence of video frames.
 12. The video system for automaticallydetecting the occurrence of an interaction event according to claim 11,wherein steps b)-d) are repeated for a second common temporal sequenceof video frames, distinct from the first common temporal sequence ofvideo frames, to determine if the interaction event has occurred betweenat least two people of the two or more people tracked within the secondcommon temporal sequence of video frames.
 13. The video system forautomatically detecting the occurrence of an interaction event accordingto claim 12, further comprising the video processing system configuredto: e) collect evidence of the detected interaction events in step d),the evidence including one or more of the number of occurrences of thedetected interaction event, the number of occurrences of the detectedinteraction event associated with each of the two or more peopledetected within the first and second common temporal sequence of videoframes, a time duration and start/end time associated with each detectedinteraction event, a calculated probability of the occurrence of theinteraction event, and an indication of static or dynamic movementassociated with each of the two or more people detected within the firstand second common temporal sequence of video frames.
 14. The videosystem for automatically detecting the occurrence of an interactionevent according to claim 13, further comprising the video processingsystem configured to: f) communicate an alert to an operativelyassociated central system, the alert indicating one or more of thenumber of occurrences of the detected interaction event, the number ofoccurrences of the detected interaction event associated with each ofthe two or more people detected within the first and second commontemporal sequence of video frames, a time duration and start/end timeassociated with each detected interaction event, a calculatedprobability of the occurrence of the interaction event, and anindication of static or dynamic movement associated with each of the twoor more people detected within the first and second common temporalsequence of video frames.
 15. The video system for automaticallydetecting the occurrence of an interaction event according to claim 11,wherein the interaction event is an illegal drug deal between two ormore people.
 16. The video system for automatically detecting theoccurrence of an interaction event according to claim 15, wherein stepsb)-d) are repeated for a second common temporal sequence of videoframes, distinct from the first common temporal sequence of videoframes, to determine if the interaction event has occurred between atleast two people of the two or more people tracked within the secondcommon temporal sequence of video frames.
 17. The video system forautomatically detecting the occurrence of an interaction event accordingto claim 16, further comprising the video processing system configuredto: e) collect evidence of the detected interaction events in step d),the evidence including one or more of the number of occurrences of thedetected interaction event, the number of occurrences of the detectedinteraction event associated with each of the two or more peopledetected within the first and second common temporal sequence of videoframes, a time duration and start/end time associated with each detectedinteraction event, a calculated probability of the occurrence of theinteraction event, and an indication of static or dynamic movementassociated with each of the two or more people detected within the firstand second common temporal sequence of video frames.
 18. The videosystem for automatically detecting the occurrence of an interactionevent according to claim 17, further comprising the video systemconfigured to: f) communicate an alert to an operatively associatedcentral system, the alert indicating one or more of the number ofoccurrences of the detected interaction event, the number of occurrencesof the detected interaction event associated with each of the two ormore people detected within the first and second common temporalsequence of video frames, a time duration and start/end time associatedwith each detected interaction event, a calculated probability of theoccurrence of the interaction event, and an indication of static ordynamic movement associated with each of the two or more people detectedwithin the first and second common temporal sequence of video frames.19. The video system for automatically detecting the occurrence of aninteraction event according to claim 11, wherein the TIFs include one ormore of a position, a velocity and a relative distance associated withthe two or more people within the first common temporal sequence ofvideo frames.
 20. The video system for automatically detecting theoccurrence of an interaction event according to claim 19, wherein thepredefined heuristics applied in step d) include the calculation of anevidence vector state, the evidence vector state calculated as afunction of a velocity threshold and a proximity threshold associatedwith the two or more people tracked within the first common temporalsequence of video frames.
 21. A video system for automatically detectingan occurrence of an interaction event of two or more objectsconcurrently present in a surveilled area, the interaction eventassociated with an illegal drug deal between the two or more objects,comprising: a video camera with an associated FOV (field-of-view)directed towards the surveilled area; and a video processing systemoperatively connected to the video camera, the video processing systemconfigured to: a) acquire a video stream from the video camera, thevideo stream including a temporal sequence of video frames including allor part of the surveilled area within all or part of the FOV associatedwith the video camera; b) detect and track two or more objects within afirst common temporal sequence of video frames included in the videostream, and generate a trajectory of each object tracked within thefirst common temporal sequence of video frames; c) process thetrajectories of the tracked objects to extract one or more trajectoryinteraction features (TIFs) associated with the trajectories of the twoor more objects tracked within the first common temporal sequence ofvideo frames, the TIFs including one or more of a position, a velocityand a relative distance associated with the two or more objects withinthe first common temporal sequence of video frames; and d) applypredefined heuristics to the extracted TIFs to detect an interactionevent has occurred between at least two objects of the two or moreobjects tracked within the first common temporal sequence of videoframes, the predefined heuristics including a velocity threshold and aproximity threshold associated with the two or more objects trackedwithin the first common temporal sequence of video frames, wherein stepsb)-d) are repeated for a second common temporal sequence of videoframes, distinct from the first common temporal sequence of videoframes, to determine if the interaction event has occurred between atleast two objects of the two or more objects tracked within the secondcommon temporal sequence of video frames.
 22. The video system forautomatically detecting the occurrence of an interaction event accordingto claim 21, further comprising the video system configured to: e)collect evidence of the detected events in step d), the evidenceincluding one or more of the number of the number of occurrences of thedetected interaction event, the number of occurrences of the detectedinteraction event associated with each of the two or more objectsdetected within the first and second common temporal sequence of videoframes, a time duration and start/end time associated with each detectedinteraction event, a calculated probability of the occurrence of theinteraction event, and an indication of static or dynamic movementassociated with each of the two or more objects detected within thefirst and second common temporal sequence of video frames.
 23. The videosystem for automatically detecting the occurrence of an interactionevent according to claim 22, further comprising a central processingsystem operatively associated with the video processing system, thevideo processing system is configured to: f) communicate an alert to thecentral processing system, the alert indicating one or more of thenumber of occurrences of the detected interaction event, the number ofoccurrences of the detected interaction event associated with each ofthe two or more objects detected within the first and second temporalsequence of video frames, a time duration and start/end time associatedwith each detected interaction event, a calculated probability of theoccurrence of the interaction event, and an indication of static ordynamic movement associated with each of the two or more objectsdetected within the first and second common temporal sequence of videoframes.